Details

Autor(en) / Beteiligte

• Heinaro, Einari
• Tanhuanpää, Topi
• Vastaranta, Mikko
• Yrttimaa, Tuomas
• Kukko, Antero
• Hakala, Teemu
• Mattsson, Teppo
• Holopainen, Markus

Titel

Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds

Ist Teil von

• Remote sensing (Basel, Switzerland), 2023-01, Vol.15 (2), p.382

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2023

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Fallen tree mapping provides valuable information regarding the ecological value of boreal forests. Airborne laser scanning (ALS) enables mapping fallen trees on a large scale. We compared the performance of line-detection-based individual fallen tree detection when using moderate point density ALS data (15 points/m2) and high-point-density unmanned aerial vehicle-based laser scanning (ULS) data (285 points/m2). Furthermore, we inspected the dataset and detection methodology-related factors impacting performance in each case. The results of this study showed that increasing the point density of the laser scanning dataset enables the detection of a larger proportion of fallen trees. However, based on our experiment, a line-detection-based fallen tree detection approach is sensitive to noise, thus generating a large number of false detections, especially with high-point-density data. Different types of filters, such as a simple height-based filter and machine-learning-based filters, can be used for reducing noise. However, using such filters is always a compromise, as in addition to reducing noise and thus false detections, they also reduce the number of true detections. Hence, a less noise-sensitive fallen tree detection method utilizing the finer details visible in high-density point clouds could be more suitable for high-point-density laser scanning data.